A reconstructed volume of a region of patient anatomy is processed to reduce motion artifacts in the reconstructed volume. Autosegmentation of high-contrast structures present in an initial reconstructed volume is performed to generate a 3D representation of the high-contrast structures. 2D mask projections are generated by performing forward projection on the 3D representation, where each 2D mask projection includes location information indicating pixels that correspond to the high-contrast structures during the forward projection process. The acquired 2D projections are modified via in-painting to generate corrected 2D projections, where the acquired 2D projections are modified using information from the 2D mask projections. For example, pixels in the acquired 2D projections that are associated with high-contrast moving structures are replaced with low-contrast pixels. These corrected 2D projections are used to produce an improved reconstructed volume with fewer and/or less visually prominent motion artifacts.

A x-ray micro tomography system provides the ability to proscriptively determine regularization parameters for iterative reconstruction of a sample, from projection data of the sample. This allows a less experienced operator to determine the regularization parameters with adequate precision.

An image is reconstructed an image from projection data by an image reconstruction computing entity. Projection data captured by an imaging device is received. An image is reconstructed and/or generated based at least in part on the projection data using a forward and back projection technique employing a convolutional spline process. The image is provided such that a user computing entity receives the image. The user computing entity is configured to display the image via a user interface thereof.

A PET image reconstruction method, including: 1) injecting a PET radioactive tracer into a biological tissue, scanning by a PET device, and detecting and counting coincidence photons to obtain an original protection data matrix; 2) establishing a measurement equation model; 3) splitting the reconstruction problem into a first sub-problem and a second sub-problem; 4) solving the first sub-problem by a filtered back-projection layer, solving the second sub-problem by an improved denoising convolutional neural network, where the filtered back-projection layer and the improved denoising convolutional neural network are connected in series to form a filtered back-projection network (FBP-Net); 5) inputting original projection data into the FBP-Net, and using an image as a tag to adjust parameters of the FBP-Net to reduce an error between an output of the FBP-Net and the tag; and 6) inputting projection data to be reconstructed into the trained FBP-Net to obtain a desired reconstructed image.

Methods and systems are provided for adaptive scan control. In one embodiment, a method includes, upon an injection of a contrast agent, performing a plurality of perfusion acquisitions of a first anatomical region of interest (ROI) of a subject with the imaging system, processing projection data of the first anatomical ROI obtained from the plurality of perfusion acquisitions to measure a contrast signal of the contrast agent, performing a plurality of angiography acquisitions, each angiography acquisition performed at a respective time determined based on the contrast signal, and performing one or more additional perfusion acquisitions between each angiography acquisition.

Methods and systems are provided for medical imaging systems. In one embodiment, a method for a medical imaging system comprises acquiring emission data during a positron emission tomography (PET) scan of a patient, reconstructing a series of live PET images while acquiring the emission data, and tracking motion of the patient during the acquiring based on the series of live PET images. In this way, patient motion during the scan may be identified and compensated for via scan acquisition and/or data processing adjustments, thereby producing a diagnostic PET image with reduced motion artifacts and increased diagnostic quality.

A method includes processing projection data with a first reconstruction algorithm and reconstructing first reconstructed volumetric image data, wherein the first reconstructed volumetric image data has a first 3D noise function. The method further includes processing the same projection data with a second different reconstruction algorithm and reconstructing second reconstructed volumetric image data, wherein the second reconstructed volumetric image data has a second 3D noise function, which is different from the first 3D noise function. The method further includes visually presenting the first or the second reconstructed volumetric image data in a main viewport. The method further includes visually presenting a sub-portion the other of the first or the second reconstructed volumetric image data in a region of interest overlaid over a sub-portion of the main viewport.

The invention refers to providing a system that allows to reduce the computational costs when using an iterative reconstructional algorithm. The system (100) comprises a providing unit (110) for providing CT projection data, a base image generation unit (120) for generating a base image based on the projection data, a modifying unit (130) for generating a modified image, wherein an image value of a voxel of the base image is modified based on the image value of the voxel, and an image reconstruction unit (140) for reconstructing an image using an iterative reconstruction algorithm that uses the modified image as a start image. Since the modifying unit is adapted to modify the base image, the base image can be modified such as to form an optimal start image for the chosen iterative reconstruction such that a faster convergence of the iterative reconstruction can be accomplished.

The present disclosure provides a system and method for CT image reconstruction. The method may include combining an analytic image reconstruction technique with an iterative reconstruction algorithm of CT images. The image reconstruction may be performed on or near a region of interest.

Automated image analysis used in vascular state modeling. Coronary vasculature in particular is modeled in some embodiments. Methods of “virtual revascularization” of a presently stenotic vasculature are described; useful, for example, as a reference in disease state determinations. Structure and uses of a model which relates records comprising acquired images or other structured data to a vascular tree representation are described.